Rice diversity panel evaluated for agro-morphological diversity by multivariate analysis

Canonical discriminant analysis indicated that the first two functions accounted for more than 86% of total variance and the traits such as days to 50% flowering, maturit[r]

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Int.J.Curr.Microbiol.App.Sci (2017) 6(11): 3887-3901

3887

Original Research Article https://doi.org/10.20546/ijcmas.2017.611.455

Rice Diversity Panel Evaluated for Agro-Morphological Diversity by Multivariate Analysis

N Vishnu Varthini1*, D Sudhakar2, M Raveendran2, S Rajeswari1, S Manonmani1, Shalini Tannidi1, P Balaji Aravindhan1, Govindaraj Ponniah1, Karthika Gunasekaran1 and S Robin1

Centre for Plant Breeding and genetics, 2Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India

*Corresponding author

A B S T R A C T

Introduction

Rice is an indispensable staple food for half of the world’s population In countries where rice is used as staple food, the per capita consumption is very high ranging from 62 to 190 kg/year (Kaiyang et al., 2008) It has the second largest production after wheat with over 503 million tonnes recorded in 2013 While the demand for rice is rising up steadily

with steep increase in human population, the land area available for rice production is shrinking due to rapid urbanization and changing life style New rice cultivars that combine high yield potential, resistance to both biotic and abiotic stress and good grain quality are urgently needed to meet future consumer demands

International Journal of Current Microbiology and Applied Sciences

ISSN: 2319-7706 Volume Number 11 (2017) pp 3887-3901

Journal homepage: http://www.ijcmas.com

Genetic diversity assessment for agro morphological traits in a population can be estimated by different methods such as univariate and multivariate analysis Multivariate analysis is utilized for analyzing more than one variable at once A diversed collection of 192 genotypes with traditional landraces and exotic genotypes from 12 countries was evaluated for 12 agro- morphological traits by multivariate analysis which reveals the pattern of genetic diversity and relationship among individuals Twelve quantitative characters i.e plant height, leaf length, number of productive tillers, panicle length, number of filled grains, spikelet fertility, days to 50% flowering; days to harvest maturity, grain length, grain width, grain length width ratio, and single plant yield were measured Multivariate techniques such as UPGMA cluster analysis, principal component analysis and canonical vector analysis was utilized to examine the variation and to estimate the relative contribution of various traits for total variability Analysis by UPGMA method had clustered 192 genotypes into seven clusters Principal component analysis had shown the genetic diversity of the population panel The cumulative variance of 80.56% of total variation among 12 characters was explained by the first five axes Canonical discriminant analysis indicated that the first two functions accounted for more than 86% of total variance and the traits such as days to 50% flowering, maturity, grain characters, panicle length and plant height were identified as principal discriminatory characters These analyses have indicated the presence of variation in the population panel which can be utilized for various crop improvement programs

K e y w o r d s Rice, Genetic variation, Agro morphological traits, Multivariate analysis, UPGMA, Principal component analysis, Canonical vector analysis

Accepted:

28 September 2017

Available Online:

10 November 2017

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3888 Genetic diversity represents the heritable variation within and between populations of organisms The success of plant breeding depends on the availability of genetic variation, knowledge about desired traits, and efficient selection strategies that make it possible to exploit existing genetic resource The pool of genetic variation within an inter-mating population is the basis for selection as well as for plant improvement

Before exploiting a population for trait improvement, it is necessary to understand the magnitude of variability in the population which is fundamental for genetic improvement in all crop species To develop segregating population, genetic distance estimates form the basis for selecting parental combinations with sufficient genetic diversity and for classifying germplasm into heterotic groups for hybrid crop breeding Population Grouping can be based on geographical origin, agro-morphological traits, pedigree information, or molecular marker data (Liakat Ali et al., 2011)

Genetic distance estimates for population grouping can be estimated by different methods as it is crucial to understand the usable variability existing in the population panel One of the approaches is to apply multivariate analysis Cluster analysis can group cultivars and meaningful information of genetic distance between genotypes and clusters can be obtained Genotypically distant parents are able to produce higher heterosis (Mian, 1989; Ghaderi et al., 1979) It is assumed that the maximum amount of heterosis is manifested in cross combination involving genotypes from the most divergent cluster (Firoz et al., 2008)

Statistical method of classification is usually by multivariate methods as it has extensive use in summarizing and describing the inherent variation among crop genotypes

Multivariate statistical tools include principal component analysis (PCA), Cluster analysis and discriminate analysis (Oyelola, 2004) Principal component analysis (PCA) can be used to uncover similarities between variable and classify the cases (genotypes), while cluster analysis on the other hand is concerned with classifying previously unclassified materials (Kaufman and Rouseeuw, 2009) Canonical discriminant analyses were used to determine the relative contribution and linear associations among the traits

It can separate among-population effects from within population effects by maximizing discrimination among populations when tested against the variation within populations (Riggs, 1973; Tai, 1989)

Multivariate analysis has been used in various crops i.e., Rice (Sanni et al., 2012, Chakravorthy et al., 2013), soybean (Bhawana Sharma and Brijvirsingh, 2012), coconut (Odewale et al., 2012), safflower, sorghum and oil palm to study the pattern of variation The study aimed to determine level of germplasm variation and identify and classify variation for grouping the accessions by taking into account several characteristics and relationship between them

Materials and Methods Experimental material

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3889 Colombia, Indonesia, Philippines, Taiwan, Uruguay, Venezuela and United States and 46 varieties and improved genotypes from different states of India constitute the population panel of 192 genotypes For easy identification and retrieval, each accession was named as RG to RG 192

Experimental site

A set of 192 genotypes were grown in Paddy Breeding Station, Department of Rice, Tamil Nadu Agricultural University, India during Rabi 2013 This area is situated at latitude of 11ºN and longitude of 77 ºE with clayey soil of pH 7.8

Methods

One hundred and ninety two genotypes were transplanted 21 days after sowing as two seedlings per hill in randomized complete block design with a spacing of 20 X 20 cm Each plot per accession consisted of four rows each 0.8 by 3.6 m long at a distance of 40 cm between the plots Normal cultural practices

were followed as per standard

recommendation

Twelve quantitative characters were measured according to methods in the descriptors for rice O sativa (IRRI, 1980) Variables considered in the descriptive and multivariate analyses were morphological (plant height, leaf length, number of productive tillers, panicle length, number of filled grains, spikelet fertility), phenological (days to 50% flowering and days to harvest maturity from the day of seeding), and grain traits (grain length, grain width, grain length width ratio, and single plant yield)

Statistical analysis

The observations recorded on 12 traits were statistically analyzed in SPSS16.0 to cluster

the genotypes based on genetic similarity Unweighted pair group method of average linkage (UPGMA) constructed by SPSS16.0 was used to classify the accessions into clusters The PCA analysis reduces the dimensions of a multivariate data to a few principal axes, generates an Eigen vector for each axis and produces component scores for the characters (Sneath and Sokal, 1973; Ariyo and Odulaja, 1991) Canonical discriminate analysis measure the axis along which variation between entries were maximum (Rezai and Frey, 1990; Ariyo, 1993)

Results and Discussion

The maximum, minimum, sum, mean, standard deviation (SD) and coefficient of variation (CV) for the measured traits are presented in table The largest variation was observed for number of productive tillers with CV of 28.03 % followed by number of filled grains per panicle (CV= 27), single plant yield (23.19), leaf length (23.02), grain length width ratio (22.16) Days to maturity has shown the least variation with the CV of 9.74%

The genotype RG1 has taken the longest days for flowering as well as maturity The taller genotype is RG20 whereas RG111 has short stature RG183 has more number of productive tillers but RG164 has higher single plant yield

Spikelet fertility ranges from 95.7% in RG131 to 54.2 in RG25 The accession with longest grain was RG57 (10.5) and largest grain width in RG160 (3.7) which is a bold grain type The slim grain type with lesser grain width was RG95 (1.5) and shortest grain was RG111 (5.8)

Cluster analysis

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3890 Landraces has diffused across the different clusters 72 % of the landraces (62 landraces) has amalgamated in cluster Cluster 1,3,4,5 and has the remaining landraces Cluster has two landraces RG1 (Mapillai samba) and RG 106 (Katta samba) Cluster has one landrace RG164 (Thillainayagam) Cluster has landraces (RG4, RG12, RG33, RG42, RG50, RG110andRG120) Nine landraces (RG32, RG73, RG97, RG109, RG155, RG163, RG168, RG179 and RG192) spread across cluster Cluster has three landraces (RG24, RG25 and RG44)

The population panel has 61 exotic genotypes which has been clustered in group (22 genotypes), group 4(29 genotypes) and each genotypes in cluster and This panel also has 47 improved genotypes and varieties from different states of India Majority of the improved genotypes and varieties (51%) has clustered in group Remaining improved genotypes and varieties has dispersed in cluster (13 genotypes), cluster 3(8 genotypes), cluster 5(1genotype) and cluster (1 genotype)

Principal component analysis

Principal component analysis has shown the genetic diversity of the population panel The cumulative variance of 80.56% by the first five axes with Eigen value of > 1.0 (Figure and 2) indicates that the identified traits within the axes exhibited great influence on the phenotype of population panel (Table and 4)

The different morphological traits contribute for total variation calculated for each component For Component which has the contribution of Days to 50% flowering (loadings -0.87), leaf length (0.78), plant height (0.765), panicle length (0.637), days to maturity (0.853) and number of filled grains (0.352) for 28.46 % of the total variability

For component 2, grain width (0.886) and grain length width ratio (0.951) has contributed 16.8 % of total variability Similarly spikelet fertility (0.771) and single plant yield (0.542), grain length (0.81), number of productive tillers (0.846) has contributed for the total variation of 14.4%, 11.7% and 9.3% from component 3, component and component respectively Canonical Discriminant analysis

Canonical discriminant analysis

simultaneously examines the differences in the morphological variables and indicates the relative contribution of each variable to accession discrimination (Vaylay and van Santen, 2002)

Quantitative variables were considered as independent and the clusters identified by cluster analysis as dependent variables The first four Discriminant functions were statistically significant according to the chi-square test at a probability of 0.01 Proper values and the distribution of their variances indicated that the first two functions accounted for more than 86% of total variance Wilks’ lambda coefficients for these two functions were precisely the lowest, indicating an almost perfect discrimination regarding the remaining functions The significant (p< 0.001) canonical correlation between the accessions and the first canonical variate (canonical correlation = 0.851) and second canonical variate (canonical correlation = 0.748) indicates that the canonical variates can explain the differentiation of the accessions

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3891 canonical Discriminant function is dominated by plant height, days to 50% flowering and days to maturity (Table 5) Number of filled grains per panicle, panicle length spikelet fertility and grain length contribute for second canonical Discriminant function It is therefore evident in the canonical discrimination that the composition of the accessions differs chiefly in days to 50% flowering, maturity, grain characters, panicle

length and plant height Centroids are discriminant score for each group when the variable means (rather than individual values for each case) are entered into the function The Proximity of group centroids indicates the errors in classification The distance between group centroids for different clusters is far away which indicates the precision of classification level (Figure 3)

Fig.1 Scattered Diagram of first two components explaining the diversity of genotypes

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Fig.3 Group centroids for different clusters is far away which indicates the precision of

classification level

Table.1 Genotypes information with clustering pattern

G NO

Genotypes Parentage Origin Cluste

r group

RG1 Mapillai samba Landrace Tamil Nadu, India

RG10

Katta samba Landrace Tamil Nadu, India

RG2 CK 275 CO50 X KAVUNI Tamil Nadu, India

RG3 Senkar Landrace Tamil Nadu, India

RG6 CHIR Improved chinsurah West Bengal

RG7 Kudaivazhai Landrace Tamil Nadu, India

RG9 Kuruvaikalanjiyam Landrace Tamil Nadu, India

RG10 Nava konmani Landrace Tamil Nadu, India

RG11 CHIR 10 Improved chinsurah West Bengal

RG13 CHIR Improved chinsurah West Bengal

RG15 Palkachaka Landrace Tamil Nadu, India

RG16 Thooyala Landrace Tamil Nadu, India

RG17 Chivapuchithiraikar Landrace Tamil Nadu, India

RG18 CHIR 11 Improved chinsurah West Bengal

RG19 Koolavalai Landrace Tamil Nadu, India

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RG21 Mohini samba Landrace Tamil Nadu, India

RG23 Koombalai Landrace Tamil Nadu, India

RG26 Rascadam Landrace Tamil Nadu, India

RG27 Muzhikaruppan Landrace Tamil Nadu, India

RG28 Kaatukuthalam Landrace Tamil Nadu, India

RG29 Vellaikattai Landrace Tamil Nadu, India

RG30 Poongar Landrace Tamil Nadu, India

RG31 Chinthamani Landrace Tamil Nadu, India

RG35 CK 143 CO50 X KAVUNI Tamil Nadu, India

RG36 Kattikar Landrace Tamil Nadu, India

RG37 Shenmolagai Landrace Tamil Nadu, India

RG38 Velli samba Landrace Tamil Nadu, India

RG39 Kaatuponni Landrace Tamil Nadu, India

RG40 kakarathan Landrace Tamil Nadu, India

RG41 Godavari samba Landrace Tamil Nadu, India

RG45 RPHP 105 Moirangphou MANIPUR

RG47 Machakantha Landrace Orissa, India

RG48 Kalarkar Landrace Tamil Nadu, India

RG49 Valanchennai Landrace Tamil Nadu, India

RG58 Kodaikuluthan Landrace Tamil Nadu, India

RG60 Rama kuruvaikar Landrace Tamil Nadu, India

RG61 Kallundai Landrace Tamil Nadu, India

RG62 Purple puttu Landrace Tamil Nadu, India

RG63 IG 71(EC 728651- 117588)

TEPI BORO::IRGC 27519-1 IRRI, Philippines

RG64 Ottadaiyan Landrace Tamil Nadu, India

RG65 IG 56 (EC 728700- 117658

BICO BRANCO Brazil

RG66 Jeevan samba Landrace Tamil Nadu, India

RG70 Karthi samba Landrace Tamil Nadu, India

RG72 Aarkadukichili Landrace Tamil Nadu, India

RG76 Mattakuruvai Landrace Tamil Nadu, India

RG77 Karuthakar Landrace Tamil Nadu, India

RG78 RPHP 165 Tilakkachari West Bengal

RG79 Manavari Landrace Tamil Nadu, India

RG82 Thooyamalli Landrace Tamil Nadu, India

RG84 Velsamba Landrace Tamil Nadu, India

RG85 RPHP 104 Kasturi (IET 8580) UTTARKHAND

RG88 Saranga Landrace Tamil Nadu, India

RG90 IG 61(EC 728731- 117696)

CRIOLLO LA FRIA Venezuela

RG91 IG 23(EC 729391- 121419)

MAHA PANNITHI::IRGC 51021-1

IRRI, Philippines

RG93 uppumolagai Landrace Tamil Nadu, India

RG94 Karthigai samba Landrace Tamil Nadu, India

RG95 Jeeraga samba Landrace Tamil Nadu, India

RG10

IG 7(EC 729598- 121648)

VARY MAINTY::1RGC 69910-1 IRRI, Philippines RG10

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